Anoop Sarkar


2020

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Effectively pretraining a speech translation decoder with Machine Translation data
Ashkan Alinejad | Anoop Sarkar
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Directly translating from speech to text using an end-to-end approach is still challenging for many language pairs due to insufficient data. Although pretraining the encoder parameters using the Automatic Speech Recognition (ASR) task improves the results in low resource settings, attempting to use pretrained parameters from the Neural Machine Translation (NMT) task has been largely unsuccessful in previous works. In this paper, we will show that by using an adversarial regularizer, we can bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities, and how this helps us effectively use a pretrained NMT decoder for speech translation.

2019

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Interrogating the Explanatory Power of Attention in Neural Machine Translation
Pooya Moradi | Nishant Kambhatla | Anoop Sarkar
Proceedings of the 3rd Workshop on Neural Generation and Translation

Attention models have become a crucial component in neural machine translation (NMT). They are often implicitly or explicitly used to justify the model’s decision in generating a specific token but it has not yet been rigorously established to what extent attention is a reliable source of information in NMT. To evaluate the explanatory power of attention for NMT, we examine the possibility of yielding the same prediction but with counterfactual attention models that modify crucial aspects of the trained attention model. Using these counterfactual attention mechanisms we assess the extent to which they still preserve the generation of function and content words in the translation process. Compared to a state of the art attention model, our counterfactual attention models produce 68% of function words and 21% of content words in our German-English dataset. Our experiments demonstrate that attention models by themselves cannot reliably explain the decisions made by a NMT model.

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Deconstructing Supertagging into Multi-Task Sequence Prediction
Zhenqi Zhu | Anoop Sarkar
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Supertagging is a sequence prediction task where each word is assigned a piece of complex syntactic structure called a supertag. We provide a novel approach to multi-task learning for Tree Adjoining Grammar (TAG) supertagging by deconstructing these complex supertags in order to define a set of related but auxiliary sequence prediction tasks. Our multi-task prediction framework is trained over the exactly same training data used to train the original supertagger where each auxiliary task provides an alternative view on the original prediction task. Our experimental results show that our multi-task approach significantly improves TAG supertagging with a new state-of-the-art accuracy score of 91.39% on the Penn Treebank supertagging dataset.

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Sign Clustering and Topic Extraction in Proto-Elamite
Logan Born | Kate Kelley | Nishant Kambhatla | Carolyn Chen | Anoop Sarkar
Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

We describe a first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script. Using hierarchical clustering, n-gram frequencies, and LDA topic models, we both replicate results obtained by manual decipherment and reveal previously-unobserved relationships between signs. This demonstrates the utility of these techniques as an aid to manual decipherment.

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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
Anoop Sarkar | Michael Strube
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials

2018

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Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing
Jetic Gū | Hassan S. Shavarani | Anoop Sarkar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The addition of syntax-aware decoding in Neural Machine Translation (NMT) systems requires an effective tree-structured neural network, a syntax-aware attention model and a language generation model that is sensitive to sentence structure. Recent approaches resort to sequential decoding by adding additional neural network units to capture bottom-up structural information, or serialising structured data into sequence. We exploit a top-down tree-structured model called DRNN (Doubly-Recurrent Neural Networks) first proposed by Alvarez-Melis and Jaakola (2017) to create an NMT model called Seq2DRNN that combines a sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. Unlike previous approaches to syntax-based NMT which use dependency parsing models our method uses constituency parsing which we argue provides useful information for translation. In addition, we use the syntactic structure of the sentence to add new connections to the tree-structured decoder neural network (Seq2DRNN+SynC). We compare our NMT model with sequential and state of the art syntax-based NMT models and show that our model produces more fluent translations with better reordering. Since our model is capable of doing translation and constituency parsing at the same time we also compare our parsing accuracy against other neural parsing models.

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Decipherment of Substitution Ciphers with Neural Language Models
Nishant Kambhatla | Anahita Mansouri Bigvand | Anoop Sarkar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.

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Prediction Improves Simultaneous Neural Machine Translation
Ashkan Alinejad | Maryam Siahbani | Anoop Sarkar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. We propose a new general-purpose prediction action which predicts future words in the input to improve quality and minimize delay in simultaneous translation. We train this agent using reinforcement learning with a novel reward function. Our agent with prediction has better translation quality and less delay compared to an agent-based simultaneous translation system without prediction.

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Prefix Lexicalization of Synchronous CFGs using Synchronous TAG
Logan Born | Anoop Sarkar
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We show that an epsilon-free, chain-free synchronous context-free grammar (SCFG) can be converted into a weakly equivalent synchronous tree-adjoining grammar (STAG) which is prefix lexicalized. This transformation at most doubles the grammar’s rank and cubes its size, but we show that in practice the size increase is only quadratic. Our results extend Greibach normal form from CFGs to SCFGs and prove new formal properties about SCFG, a formalism with many applications in natural language processing.

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Simultaneous Translation using Optimized Segmentation
Maryam Siahbani | Hassan Shavarani | Ashkan Alinejad | Anoop Sarkar
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Decipherment for Adversarial Offensive Language Detection
Zhelun Wu | Nishant Kambhatla | Anoop Sarkar
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and unseen expressions especially in an adversarial setting where the users can repeatedly try to bypass the filter. In this paper, we model the disguised messages as if they are produced by encrypting the original message using an invented cipher. We apply automatic decipherment techniques to decode the disguised malicious text, which can be then filtered using rules or classifiers. We provide experimental results on three different datasets and show that decipherment is an effective tool for this task.

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In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition
Golnar Sheikhshabbafghi | Inanc Birol | Anoop Sarkar
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Rapidly expanding volume of publications in the biomedical domain makes it increasingly difficult for a timely evaluation of the latest literature. That, along with a push for automated evaluation of clinical reports, present opportunities for effective natural language processing methods. In this study we target the problem of named entity recognition, where texts are processed to annotate terms that are relevant for biomedical studies. Terms of interest in the domain include gene and protein names, and cell lines and types. Here we report on a pipeline built on Embeddings from Language Models (ELMo) and a deep learning package for natural language processing (AllenNLP). We trained context-aware token embeddings on a dataset of biomedical papers using ELMo, and incorporated these embeddings in the LSTM-CRF model used by AllenNLP for named entity recognition. We show these representations improve named entity recognition for different types of biomedical named entities. We also achieve a new state of the art in gene mention detection on the BioCreative II gene mention shared task.

2017

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Lexicalized Reordering for Left-to-Right Hierarchical Phrase-based Translation
Maryam Siahbani | Anoop Sarkar
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Phrase-based and hierarchical phrase-based (Hiero) translation models differ radically in the way reordering is modeled. Lexicalized reordering models play an important role in phrase-based MT and such models have been added to CKY-based decoders for Hiero. Watanabe et al. (2006) proposed a promising decoding algorithm for Hiero (LR-Hiero) that visits input spans in arbitrary order and produces the translation in left to right (LR) order which leads to far fewer language model calls and leads to a considerable speedup in decoding. We introduce a novel shift-reduce algorithm to LR-Hiero to decode with our lexicalized reordering model (LRM) and show that it improves translation quality for Czech-English, Chinese-English and German-English.

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Joint Prediction of Word Alignment with Alignment Types
Anahita Mansouri Bigvand | Te Bu | Anoop Sarkar
Transactions of the Association for Computational Linguistics, Volume 5

Current word alignment models do not distinguish between different types of alignment links. In this paper, we provide a new probabilistic model for word alignment where word alignments are associated with linguistically motivated alignment types. We propose a novel task of joint prediction of word alignment and alignment types and propose novel semi-supervised learning algorithms for this task. We also solve a sub-task of predicting the alignment type given an aligned word pair. In our experimental results, the generative models we introduce to model alignment types significantly outperform the models without alignment types.

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Coordination in TAG without the Conjoin Operation
Chung-hye Han | Anoop Sarkar
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

2016

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Graph-based Semi-supervised Gene Mention Tagging
Golnar Sheikhshab | Elizabeth Starks | Aly Karsan | Anoop Sarkar | Inanc Birol
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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The Challenge of Simultaneous Speech Translation
Anoop Sarkar
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks

2015

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Improving Statistical Machine Translation with a Multilingual Paraphrase Database
Ramtin Mehdizadeh Seraj | Maryam Siahbani | Anoop Sarkar
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Rada Mihalcea | Joyce Chai | Anoop Sarkar
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Two Improvements to Left-to-Right Decoding for Hierarchical Phrase-based Machine Translation
Maryam Siahbani | Anoop Sarkar
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Efficient Left-to-Right Hierarchical Phrase-Based Translation with Improved Reordering
Maryam Siahbani | Baskaran Sankaran | Anoop Sarkar
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Ensemble Triangulation for Statistical Machine Translation
Majid Razmara | Anoop Sarkar
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Scalable Variational Inference for Extracting Hierarchical Phrase-based Translation Rules
Baskaran Sankaran | Gholamreza Haffari | Anoop Sarkar
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views
Ann Clifton | Max Whitney | Anoop Sarkar
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Graph Propagation for Paraphrasing Out-of-Vocabulary Words in Statistical Machine Translation
Majid Razmara | Maryam Siahbani | Reza Haffari | Anoop Sarkar
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Stacking for Statistical Machine Translation
Majid Razmara | Anoop Sarkar
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Multi-Metric Optimization Using Ensemble Tuning
Baskaran Sankaran | Anoop Sarkar | Kevin Duh
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Improved Reordering for Shallow-n Grammar based Hierarchical Phrase-based Translation
Baskaran Sankaran | Anoop Sarkar
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Bootstrapping via Graph Propagation
Max Whitney | Anoop Sarkar
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Mixing Multiple Translation Models in Statistical Machine Translation
Majid Razmara | George Foster | Baskaran Sankaran | Anoop Sarkar
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Kriya - The SFU System for Translation Task at WMT-12
Majid Razmara | Baskaran Sankaran | Ann Clifton | Anoop Sarkar
Proceedings of the Seventh Workshop on Statistical Machine Translation

2011

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Bayesian Extraction of Minimal SCFG Rules for Hierarchical Phrase-based Translation
Baskaran Sankaran | Gholamreza Haffari | Anoop Sarkar
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Combining Morpheme-based Machine Translation with Post-processing Morpheme Prediction
Ann Clifton | Anoop Sarkar
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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An Ensemble Model that Combines Syntactic and Semantic Clustering for Discriminative Dependency Parsing
Gholamreza Haffari | Marzieh Razavi | Anoop Sarkar
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Book Reviews: Parsing Schemata for Practical Text Analysis by Carlos Gómez Rodríguez
Anoop Sarkar
Computational Linguistics, Volume 37, Issue 4 - December 2011

2010

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Incremental Decoding for Phrase-Based Statistical Machine Translation
Baskaran Sankaran | Ajeet Grewal | Anoop Sarkar
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Mirella Lapata | Anoop Sarkar
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

2009

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Exploration of the LTAG-Spinal Formalism and Treebank for Semantic Role Labeling
Yudong Liu | Anoop Sarkar
Proceedings of the 2009 Workshop on Grammar Engineering Across Frameworks (GEAF 2009)

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Active Learning for Multilingual Statistical Machine Translation
Gholamreza Haffari | Anoop Sarkar
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Active Learning for Statistical Phrase-based Machine Translation
Gholamreza Haffari | Maxim Roy | Anoop Sarkar
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Ulrich Germann | Chirag Shah | Svetlana Stoyanchev | Carolyn Penstein Rosé | Anoop Sarkar
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

2008

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Training a Perceptron with Global and Local Features for Chinese Word Segmentation
Dong Song | Anoop Sarkar
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

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Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)
Claire Gardent | Anoop Sarkar
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

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Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
Gholamreza Haffari | Anoop Sarkar
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Exploiting Rich Syntactic Information for Relationship Extraction from Biomedical Articles
Yudong Liu | Zhongmin Shi | Anoop Sarkar
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Simultaneous Identification of Biomedical Named-Entity and Functional Relation Using Statistical Parsing Techniques
Zhongmin Shi | Anoop Sarkar | Fred Popowich
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Active Learning for the Identification of Nonliteral Language
Julia Birke | Anoop Sarkar
Proceedings of the Workshop on Computational Approaches to Figurative Language

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Transductive learning for statistical machine translation
Nicola Ueffing | Gholamreza Haffari | Anoop Sarkar
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Experimental Evaluation of LTAG-Based Features for Semantic Role Labeling
Yudong Liu | Anoop Sarkar
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Voting between Dictionary-Based and Subword Tagging Models for Chinese Word Segmentation
Dong Song | Anoop Sarkar
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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Using LTAG-Based Features for Semantic Role Labeling
Yudong Liu | Anoop Sarkar
Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms

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A Clustering Approach for Nearly Unsupervised Recognition of Nonliteral Language
Julia Birke | Anoop Sarkar
11th Conference of the European Chapter of the Association for Computational Linguistics

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Tutorial on Inductive Semi-supervised Learning Methods: with Applicability to Natural Language Processing
Anoop Sarkar | Gholamreza Haffari
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Tutorial Abstracts

2004

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A Smorgasbord of Features for Statistical Machine Translation
Franz Josef Och | Daniel Gildea | Sanjeev Khudanpur | Anoop Sarkar | Kenji Yamada | Alex Fraser | Shankar Kumar | Libin Shen | David Smith | Katherine Eng | Viren Jain | Zhen Jin | Dragomir Radev
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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Discriminative Reranking for Machine Translation
Libin Shen | Anoop Sarkar | Franz Josef Och
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

2003

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Using LTAG Based Features in Parse Reranking
Libin Shen | Anoop Sarkar | Aravind Joshi
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

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Bootstrapping statistical parsers from small datasets
Mark Steedman | Miles Osborne | Anoop Sarkar | Stephen Clark | Rebecca Hwa | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
10th Conference of the European Chapter of the Association for Computational Linguistics

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Example Selection for Bootstrapping Statistical Parsers
Mark Steedman | Rebecca Hwa | Stephen Clark | Miles Osborne | Anoop Sarkar | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

2002

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Squibs and Discussions: A Note on Typing Feature Structures
Shuly Wintner | Anoop Sarkar
Computational Linguistics, Volume 28, Number 3, September 2002

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Statistical Morphological Tagging and Parsing of Korean with an LTAG Grammar
Anoop Sarkar | Chung-Hye Han
Proceedings of the Sixth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+6)

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Learning Verb Argument Structure from Minimally Annotated Corpora
Anoop Sarkar | Woottiporn Tripasai
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Applying Co-Training Methods to Statistical Parsing
Anoop Sarkar
Second Meeting of the North American Chapter of the Association for Computational Linguistics

2000

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Learning Verb Subcategorization from Corpora: Counting Frame Subsets
Daniel Zeman | Anoop Sarkar
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Automatic Extraction of Subcategorization Frames for Czech
Anoop Sarkar | Daniel Zeman
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Some Experiments on Indicators of Parsing Complexity for Lexicalized Grammars
Anoop Sarkar | Fei Xia | Aravind Joshi
Proceedings of the COLING-2000 Workshop on Efficiency In Large-Scale Parsing Systems

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Practical experiments in parsing using Tree Adjoining Grammars
Anoop Sarkar
Proceedings of the Fifth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+5)

1998

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Prefix Probabilities from Stochastic Tree Adjoining Grammars
Mark-Jan Nederhof | Anoop Sarkar | Giorgio Satta
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Conditions on Consistency of Probabilistic Tree Adjoining Grammars
Anoop Sarkar
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Prefix Probabilities from Stochastic Free Adjoining Grammars
Mark-Jan Nederhof | Anoop Sarkar | Giorgio Satta
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Conditions on Consistency of Probabilistic Tree Adjoining Grammars
Anoop Sarkar
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Prefix probabilities for linear indexed grammars
Mark-Jan Nederhof | Anoop Sarkar | Giorgio Satta
Proceedings of the Fourth International Workshop on Tree Adjoining Grammars and Related Frameworks (TAG+4)

1997

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Maintaining the Forest and Burning out the Underbrush in XTAG
Christine Doran | Beth Hockey | Philip Hopely | Joseph Rosenzweig | Anoop Sarkar | B. Srinivas | Fei Xia
Computational Environments for Grammar Development and Linguistic Engineering

1996

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Incremental Parser Generation for Tree Adjoining Grammars
Anoop Sarkar
34th Annual Meeting of the Association for Computational Linguistics

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Coordination in Tree Adjoining Grammars: Formalization and Implementation
Anoop Sarkar | Aravind Joshi
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

1995

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University of Pennsylvania: Description of the University of Pennsylvania System Used for MUC-6
Breck Baldwin | Jeff Reynar | Mike Collins | Jason Eisner | Adwait Ratnaparkhi | Joseph Rosenzweig | Anoop Sarkar | Srinivas
Sixth Message Understanding Conference (MUC-6): Proceedings of a Conference Held in Columbia, Maryland, November 6-8, 1995

1993

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Extending Kimmo’s Two-Level Model of Morphology
Anoop Sarkar
31st Annual Meeting of the Association for Computational Linguistics